Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
1,458
result(s) for
"Renewable energy sources Computer simulation."
Sort by:
Secondary load frequency control for multi-microgrids: HiL real-time simulation
The intermittent feature of renewable energy sources leads to the mismatch between supply and load demand on microgrids. In such circumstance, the system experiences large fluctuations, if the secondary load frequency control (LFC) mechanism is unable to compensate the mismatch. In this issue, this paper presents a well-structured combination of the fuzzy PD and cascade PI-PD controllers named FPD/PI-PD controller as a supplementary (secondary) controller for the secondary load frequency control in the islanded multi-microgrid (MMG). Additionally, two modifications to the JAYA algorithm are made to enhance the diversity of the initial population and ameliorate the global searching ability in the iterative process. Afterward, the improved JAYA algorithm, referred to as IJAYA, is employed for fine-tuning the proposed structured controller installed in areas of the studied MMG. The superiority of the proposed IJAYA is validated by comparative analysis with genetic algorithm and basic JAYA in a similar structure of the PID controller. Furthermore, it will be shown that the proposed FPD/PI-PD controller employing IJAYA provides a higher degree of stability in suppressing the responses deviations as compared with the conventional PID and FPID controller structures. Finally, the novel optimal proposed approach is validated and implemented in the hardware-in-the-loop (HIL) based on OPAL-RT to integrate the fidelity of physical simulation and the flexibility of numerical simulation.
Journal Article
Adversarial super-resolution of climatological wind and solar data
Accurate and high-resolution data reflecting different climate scenarios are vital for policy makers when deciding on the development of future energy resources, electrical infrastructure, transportation networks, agriculture, and many other societally important systems. However, state-of-the-art long-term global climate simulations are unable to resolve the spatiotemporal characteristics necessary for resource assessment or operational planning. We introduce an adversarial deep learning approach to super resolve wind velocity and solar irradiance outputs from global climate models to scales sufficient for renewable energy resource assessment. Using adversarial training to improve the physical and perceptual performance of our networks, we demonstrate up to a 50resolution enhancement of wind and solar data. In validation studies, the inferred fields are robust to input noise, possess the correct small-scale properties of atmospheric turbulent flow and solar irradiance, and retain consistency at large scales with coarse data. An additional advantage of our fully convolutional architecture is that it allows for training on small domains and evaluation onarbitrarily-sized inputs, including global scale. We conclude with a super-resolution study of renewable energy resources based on climate scenario data fromthe Intergovernmental Panel on Climate Change’s Fifth Assessment Report.
Journal Article
Simultaneous optimization of renewable energy and energy storage capacity with hierarchical control
To fully consider the complementary role of different energy sources and reduce the curtailment of renewable energy (RE) in high RE penetration systems, a hierarchical optimization algorithm is proposed to simultaneously optimize the capacity of RE generation and energy storage systems (ESS). Time sequence simulation (TSS) technology is adopted to fully consider the regional RE resource characteristics and make the model more reliable. An optimization model for evaluating ESS capacity is established at a lower level. To overcome the high dimensional complexity of time sequence data, this paper re-formulates this sub-model as a consensus problem, which can be solved by a distributed approach to minimize the system's total investment costs. At the upper level, the model for assessing the proportion of wind and solar capacity is developed by maximizing the RE generation. The golden section Fibonacci tree optimization (GSFTO) algorithm is utilized to improve the efficiency and solution accuracy. The results show that the algorithm and model are feasible and applicable for the identified purposes, which can provide a useful guidance for the development of power generation and the energy storage capacity in high RE penetration systems.
Journal Article
Model predictive control of consensus-based energy management system for DC microgrid
The increasing deployment and exploitation of distributed renewable energy source (DRES) units and battery energy storage systems (BESS) in DC microgrids lead to a promising research field currently. Individual DRES and BESS controllers can operate as grid-forming (GFM) or grid-feeding (GFE) units independently, depending on the microgrid operational requirements. In standalone mode, at least one controller should operate as a GFM unit. In grid-connected mode, all the controllers may operate as GFE units. This article proposes a consensus-based energy management system based upon Model Predictive Control (MPC) for DRES and BESS individual controllers to operate in both configurations (GFM or GFE). Energy management system determines the mode of power flow based on the amount of generated power, load power, solar irradiance, wind speed, rated power of every DG, and state of charge (SOC) of BESS. Based on selection of power flow mode, the role of DRES and BESS individual controllers to operate as GFM or GFE units, is decided. MPC hybrid cost function with auto-tuning weighing factors will enable DRES and BESS converters to switch between GFM and GFE. In this paper, a single hybrid cost function has been proposed for both GFM and GFE. The performance of the proposed energy management system has been validated on an EU low voltage benchmark DC microgrid by MATLAB/SIMULINK simulation and also compared with Proportional Integral (PI) & Sliding Mode Control (SMC) technique. It has been noted that as compared to PI & SMC, MPC technique exhibits settling time of less than 1μsec and 5% overshoot.
Journal Article
Artificial intelligence strategies for simulating the integrated energy systems
In recent decades, the operational impact of Artificial Intelligence (AI) strategies is massively dominating the scientific arena of improving the operation of energy systems and their hybrid integrations. Comprehensively, this paper highlights the firm methodological link of AI strategies with the different defined categories of numerical methods in hypothetically simulating the complex integrated energy systems especially the integration of Renewable Energy Sources (RES). The conducted studies in this paper are related to the bifurcations of the applied numerical simulation methodologies for efficient energy systems and the practical implementations of the optimal operated energy systems considering the integration scenarios of these methodologies with AI strategies. Furthermore, this research reviews innovatively several case studies and practical examples to emphasize the effective contributions of AI strategies in enhancing the computational analysis of numerical simulation methods forming a smart approach for assessing experimental studies that are associated with energy systems. Finally, this paper deeply discusses the concept of integration either in the hybrid controlling strategies combining AI with numerical simulation methods or in combining different energy systems in one hybrid model for reliable operation considering the complexity level.
Journal Article
Influence of internal variability on future changes in surface wind speed in China with two large ensemble simulations
Wind energy, as one of the renewable energy sources, plays a crucial role in the global energy system’s transition to clean energy. China possesses vast and widely distributed wind energy resources, and in recent years, it has rapidly developed and begun large-scale commercial utilization. Therefore, studying changes in surface wind speeds (SWSs) is highly important for wind energy development in China. This study utilizes two initial condition large ensemble simulations to project future changes in SWSs over China. The two sets of initial large ensemble models used are CanESM2-LE and CESM1-LE. By comparing the results from these two large ensemble models, the influence of internal variability of the climate system on SWSs in China are studied. Both models can effectively reproduce the climatological spatial distribution of SWSs in reanalysis. Results from both models indicate that external forcing leads to an increase in winter SWSs in eastern China, while SWSs decreases in the southeastern coastal areas and southwestern Tibet. In summer, SWSs exhibits a pattern of decrease in the north and increase in the south. The magnitude of wind speed changes is greater in winter than in summer. Additionally, as the projected period extends, the magnitude of these changes intensifies. The research results can provide a scientific basis for the future planning of wind power deployment.
Journal Article
Optimization the stochastic optimal reactive power dispatch with renewable energy resources using a modified dandelion algorithm
Improvement performance of transmission systems is crucial task that can be boosted via optimal reactive power dispatch (ORPD). However, the continuous variations of load demand and the power produced by the renewable energy sources (RERs) increases the complicities of solving the stochastic optimal reactive power dispatch (SORPD) solution. In this regard, a modified Dandelion Optimizer (MDO) algorithm is introduced to optimize the SORPD solution with taking into consideration the stochastic fluctuations or the random variations of the load demand and the power produced by RERs. The suggested MDO depends upon developing the searching exploration and exploitation abilities by integration of three methodologies involving the Quasi-oppositional-based-learning (QOBL), the Weibull flight motion strategy (WFM) and the fitness distance balance (FDB). The SORPD is solved for IEEE 30-bus system to reduce summation of expected power losses (SEPL) and enhance the summation of expected voltage stability (SEVS) with and without integration RERs. The uncertainties of the load demand and the power produced by the RERs are represented using Monte Carlo simulations and scenario reduction approach in which 15 scenarios are generated to model the stochastic nature of the load demand and the power produced by RERs. The simulation results reveal to that application the proposed algorithm for SORPD can reduce the SEPL and improve SEVS considerably, especially with integration of the RERs. The Comparative results demonstrate that the MDO algorithm is the best for solution the SORPD against sand cat swarm optimization (SCSO), gorilla troop optimizer (GTO), harmony search (HS), and Beluga whale optimization (BWO).
Journal Article
Artificial neural network-based output power prediction of grid-connected semitransparent photovoltaic system
The solar photovoltaic system is an emerging renewable energy resource. The performance of the solar photovoltaic system is predicted based on the historical experimental dataset. In this work, the real-time prediction models are developed for the output power prediction of the STPV system. The performance of the semitransparent photovoltaic system is predicted for the Kovilpatti region where the climatic condition is hot and humid. The short-term power is predicted for the hourly, daily, and weekly average are considered. The feature selected for the prediction of the output power of the STPV system comprises of the solar radiation, ambient temperature, and wind velocity of the Kovilpatti region. The result reveals that the output power prediction of the hourly, daily, and weekly power have the very high value of the correlation coefficient of
R
. The final model produced accurate forecasts, with a Root mean square (RMSE) of 0.25 in ELMAN and 0.30 in FFN and 0.426 in GRN. These features of the training algorithm indicate that the model is not dependent on the model’s position or configuration in the simulation.
Journal Article